Building applications that perceive the world around them is challenging. A developer needs to (a) select and develop corresponding machine learning algorithms and models, (b) build a series of prototypes and demos, (c) balance resource consumption against the quality of the solutions, and finally (d) identify and mitigate problematic cases. The MediaPipe framework addresses all of these challenges. A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms. We show that these features enable a developer to focus on the algorithm or model development and use MediaPipe as an environment for iteratively improving their application with results reproducible across different devices and platforms. MediaPipe will be open-sourced at https://github.com/google/mediapipe.

This paper revisits the communication complexity of largescale 3D fast Fourier transforms (FFTs) and asks what impact trends in current architectures will have on FFT performance at exascale. We analyze both memory hierarchy traffic and network communication to derive suitable analytical models, which we calibrate against current software implementations; we then evaluate models to make predictions about potential scaling outcomes at exascale, based on extrapolating current technology trends. Of particular interest is the performance impact of choosing high-density processors, typified today by graphics co-processors (GPUs), as the base processor for an exascale system. Among various observations, a key prediction is that although inter-node all-to-all communication is expected to be the bottleneck of distributed FFTs, intra-node communication-expressed precisely in terms of the relative balance among compute capacity, memory bandwidth, and network bandwidth-will play a critical role.

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We present a novel data-driven technique for radiometric self-calibration of video from an unknown camera. Our approach self-calibrates radiometric variations in video, and is applied as a post-process; there is no need to access the camera, and in particular it is applicable to internet videos. This technique builds on empirical evidence that in video the camera response function (CRF) should be regarded time variant, as it changes with scene content and exposure, instead of relying on a single camera response function. We show that a time-varying mixture of responses produces better accuracy and consistently reduces the error in mapping intensity to irradiance when compared to a single response model. Furthermore, our mixture model counteracts the effects of possible nonlinear exposure-dependent intensity perturbations and white-balance changes caused by proprietary camera firmware. We further show how radiometrically calibrated video improves the performance of other video analysis algorithms, enabling a video segmentation algorithm to be invariant to exposure and gain variations over the sequence. We validate our data-driven technique on videos from a variety of cameras and demonstrate the generality of our approach by applying it to internet video.

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